{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled1.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/arbi11/CompEM1/blob/master/1_pandas_RF.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3bFvjelQUM2Q",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3WWFHPxCVTN7",
        "colab_type": "text"
      },
      "source": [
        "# 1. import the necessary packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1xlIH4yzUUs_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import scipy\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tkWZ8f4LUWmK",
        "colab_type": "text"
      },
      "source": [
        "# 2. Specify the filepath if you don't want to work with relative paths"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pipevEDNUnSN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "8039802d-8ea8-4297-81ed-e3db1de386c1"
      },
      "source": [
        "! git clone https://github.com/arbi11/CompEM1.git"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'CompEM1'...\n",
            "remote: Enumerating objects: 3, done.\u001b[K\n",
            "remote: Counting objects: 100% (3/3), done.\u001b[K\n",
            "remote: Compressing objects: 100% (3/3), done.\u001b[K\n",
            "remote: Total 3 (delta 0), reused 3 (delta 0), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (3/3), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A9DIBQb2Usvf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "0001c99c-f1e2-4c5c-c6e3-9155a3564e88"
      },
      "source": [
        "! ls"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CompEM1  sample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H634sIdGUuW7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8e5b8e8a-75a6-42ff-9694-8bba040e4f25"
      },
      "source": [
        "cd CompEM1/\n"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/CompEM1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ACbKMaadUxAX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "189a52c6-d6ff-4a75-d466-78bc7286f169"
      },
      "source": [
        "! ls"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "indian_liver_patient.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jr1KOl2qU6wv",
        "colab_type": "text"
      },
      "source": [
        "In case you have a file on your local system\n",
        "\n",
        "file_path = 'D:\\\\Documents\\\\Codes\\\\Kabeer\\\\Liver\\\\'\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UQ_zl-M5VM5a",
        "colab_type": "text"
      },
      "source": [
        "# 3. read the data -- into a pandas dataframe "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yyl2zJ6KUebK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "# 3. read the data -- into a pandas dataframe \n",
        "data_frame = pd.read_csv('indian_liver_patient.csv')\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aBCvW7S7VX1W",
        "colab_type": "text"
      },
      "source": [
        "# 4. Some data description and analysis\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "af7DAXOZUilp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "outputId": "f6c885b5-2072-4aa5-9c1a-275c63c47fc4"
      },
      "source": [
        "# 4. Some data description and analysis\n",
        "data_frame.columns # prints the name of all the columns in ur table\n",
        "\n",
        "data_frame.dtypes # the data type of all the columns\n",
        "\n",
        "data_frame.describe() # basic statistics about your data\n",
        "\n",
        "data_frame.describe(include= 'all') # basic statistics about your data\n",
        "\n",
        "# count the number of males & females\n",
        "data_frame['Gender'].value_counts()\n",
        "\n",
        "# to convert categorical variables to quantitative (one-hot encoding)\n",
        "dataframes = pd.get_dummies(data_frame['Gender'])\n",
        "\n",
        "data_frame.head() # prints out first 5 rows of the data\n",
        "\n",
        "sns.boxplot(x = 'Gender', y = 'Albumin', data= data_frame)\n",
        "sns.boxplot(x = 'Gender', y = 'Total_Protiens', data= data_frame)\n"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Total_Bilirubin</th>\n",
              "      <th>Direct_Bilirubin</th>\n",
              "      <th>Alkaline_Phosphotase</th>\n",
              "      <th>Alamine_Aminotransferase</th>\n",
              "      <th>Aspartate_Aminotransferase</th>\n",
              "      <th>Total_Protiens</th>\n",
              "      <th>Albumin</th>\n",
              "      <th>Albumin_and_Globulin_Ratio</th>\n",
              "      <th>Dataset</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>583.000000</td>\n",
              "      <td>583.000000</td>\n",
              "      <td>583.000000</td>\n",
              "      <td>583.000000</td>\n",
              "      <td>583.000000</td>\n",
              "      <td>583.000000</td>\n",
              "      <td>583.000000</td>\n",
              "      <td>583.000000</td>\n",
              "      <td>579.000000</td>\n",
              "      <td>583.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>44.746141</td>\n",
              "      <td>3.298799</td>\n",
              "      <td>1.486106</td>\n",
              "      <td>290.576329</td>\n",
              "      <td>80.713551</td>\n",
              "      <td>109.910806</td>\n",
              "      <td>6.483190</td>\n",
              "      <td>3.141852</td>\n",
              "      <td>0.947064</td>\n",
              "      <td>1.286449</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>16.189833</td>\n",
              "      <td>6.209522</td>\n",
              "      <td>2.808498</td>\n",
              "      <td>242.937989</td>\n",
              "      <td>182.620356</td>\n",
              "      <td>288.918529</td>\n",
              "      <td>1.085451</td>\n",
              "      <td>0.795519</td>\n",
              "      <td>0.319592</td>\n",
              "      <td>0.452490</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>63.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>2.700000</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>33.000000</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>175.500000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>5.800000</td>\n",
              "      <td>2.600000</td>\n",
              "      <td>0.700000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>45.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>208.000000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>42.000000</td>\n",
              "      <td>6.600000</td>\n",
              "      <td>3.100000</td>\n",
              "      <td>0.930000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>58.000000</td>\n",
              "      <td>2.600000</td>\n",
              "      <td>1.300000</td>\n",
              "      <td>298.000000</td>\n",
              "      <td>60.500000</td>\n",
              "      <td>87.000000</td>\n",
              "      <td>7.200000</td>\n",
              "      <td>3.800000</td>\n",
              "      <td>1.100000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>90.000000</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>19.700000</td>\n",
              "      <td>2110.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "      <td>4929.000000</td>\n",
              "      <td>9.600000</td>\n",
              "      <td>5.500000</td>\n",
              "      <td>2.800000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Age  Total_Bilirubin  ...  Albumin_and_Globulin_Ratio     Dataset\n",
              "count  583.000000       583.000000  ...                  579.000000  583.000000\n",
              "mean    44.746141         3.298799  ...                    0.947064    1.286449\n",
              "std     16.189833         6.209522  ...                    0.319592    0.452490\n",
              "min      4.000000         0.400000  ...                    0.300000    1.000000\n",
              "25%     33.000000         0.800000  ...                    0.700000    1.000000\n",
              "50%     45.000000         1.000000  ...                    0.930000    1.000000\n",
              "75%     58.000000         2.600000  ...                    1.100000    2.000000\n",
              "max     90.000000        75.000000  ...                    2.800000    2.000000\n",
              "\n",
              "[8 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-1GC73BMVahl",
        "colab_type": "text"
      },
      "source": [
        "# 5. Data correlation\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LgX9V3WZUkBR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "outputId": "04106870-ceb4-4dd1-b31e-5c1c0c401659"
      },
      "source": [
        "\n",
        "# 5. Data correlation\n",
        "data_frame.corr(method= 'pearson')\n",
        "\n",
        "sns.heatmap(data_frame.corr(method= 'pearson'))\n",
        "\n",
        "data_frame['Albumin_and_Globulin_Ratio'].isna()\n",
        "\n",
        "\n",
        "index = data_frame['Albumin_and_Globulin_Ratio'].index[data_frame['Albumin_and_Globulin_Ratio'].apply(np.isnan)]\n"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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aKlsVVgQ+BYwDJgFnA5sCm0uqNrvZ+sC56Zz/leIDXAp83/YWwGzgNNvXANOA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\nYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2cHtVUsIVncS",
        "colab_type": "text"
      },
      "source": [
        "# 6. Data manipulation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UhiTHr2aXEwz",
        "colab_type": "text"
      },
      "source": [
        "filling in the missing values in column - 'Albumin_and_Globulin_Ratio'"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u7v3CQggVdtJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data_frame = data_frame.fillna(data_frame.mean())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AmIz4N4AXKck",
        "colab_type": "text"
      },
      "source": [
        "Another way of doing it by just concentrating that particular column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "icAfz-_NWqeO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data_frame['Albumin_and_Globulin_Ratio'].fillna((data_frame['Albumin_and_Globulin_Ratio'].mean()), inplace= True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ySseBz4PVkBO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "outputId": "eac129a6-d85f-4dfb-ece5-33c4d86fc635"
      },
      "source": [
        "data_frame.describe()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Total_Bilirubin</th>\n",
              "      <th>Direct_Bilirubin</th>\n",
              "      <th>Alkaline_Phosphotase</th>\n",
              "      <th>Alamine_Aminotransferase</th>\n",
              "      <th>Aspartate_Aminotransferase</th>\n",
              "      <th>Total_Protiens</th>\n",
              "      <th>Albumin</th>\n",
              "      <th>Albumin_and_Globulin_Ratio</th>\n",
              "      <th>Dataset</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>583.000000</td>\n",
              "      <td>583.000000</td>\n",
              "      <td>583.000000</td>\n",
              "      <td>583.000000</td>\n",
              "      <td>583.000000</td>\n",
              "      <td>583.000000</td>\n",
              "      <td>583.000000</td>\n",
              "      <td>583.000000</td>\n",
              "      <td>583.000000</td>\n",
              "      <td>583.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>44.746141</td>\n",
              "      <td>3.298799</td>\n",
              "      <td>1.486106</td>\n",
              "      <td>290.576329</td>\n",
              "      <td>80.713551</td>\n",
              "      <td>109.910806</td>\n",
              "      <td>6.483190</td>\n",
              "      <td>3.141852</td>\n",
              "      <td>0.947064</td>\n",
              "      <td>1.286449</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>16.189833</td>\n",
              "      <td>6.209522</td>\n",
              "      <td>2.808498</td>\n",
              "      <td>242.937989</td>\n",
              "      <td>182.620356</td>\n",
              "      <td>288.918529</td>\n",
              "      <td>1.085451</td>\n",
              "      <td>0.795519</td>\n",
              "      <td>0.318492</td>\n",
              "      <td>0.452490</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>63.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>2.700000</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>33.000000</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>175.500000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>5.800000</td>\n",
              "      <td>2.600000</td>\n",
              "      <td>0.700000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>45.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>208.000000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>42.000000</td>\n",
              "      <td>6.600000</td>\n",
              "      <td>3.100000</td>\n",
              "      <td>0.947064</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>58.000000</td>\n",
              "      <td>2.600000</td>\n",
              "      <td>1.300000</td>\n",
              "      <td>298.000000</td>\n",
              "      <td>60.500000</td>\n",
              "      <td>87.000000</td>\n",
              "      <td>7.200000</td>\n",
              "      <td>3.800000</td>\n",
              "      <td>1.100000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>90.000000</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>19.700000</td>\n",
              "      <td>2110.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "      <td>4929.000000</td>\n",
              "      <td>9.600000</td>\n",
              "      <td>5.500000</td>\n",
              "      <td>2.800000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Age  Total_Bilirubin  ...  Albumin_and_Globulin_Ratio     Dataset\n",
              "count  583.000000       583.000000  ...                  583.000000  583.000000\n",
              "mean    44.746141         3.298799  ...                    0.947064    1.286449\n",
              "std     16.189833         6.209522  ...                    0.318492    0.452490\n",
              "min      4.000000         0.400000  ...                    0.300000    1.000000\n",
              "25%     33.000000         0.800000  ...                    0.700000    1.000000\n",
              "50%     45.000000         1.000000  ...                    0.947064    1.000000\n",
              "75%     58.000000         2.600000  ...                    1.100000    2.000000\n",
              "max     90.000000        75.000000  ...                    2.800000    2.000000\n",
              "\n",
              "[8 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    }
  ]
}
